In real dialogue scenarios, as there are unknown input noises in the
utterances, existing supervised slot filling models often perform poorly in
practical applications. Even though there are some studies on noise-robust
models, these works are only evaluated on rule-based synthetic datasets, which
is limiting, making it difficult to promote the research of noise-robust
methods. In this paper, we introduce a noise robustness evaluation dataset
named Noise-SF for slot filling task. The proposed dataset contains five types
of human-annotated noise, and all those noises are exactly existed in real
extensive robust-training methods of slot filling into the proposed framework.
By conducting exhaustive empirical evaluation experiments on Noise-SF, we find
that baseline models have poor performance in robustness evaluation, and the
proposed framework can effectively improve the robustness of models. Based on
the empirical experimental results, we make some forward-looking suggestions to
fuel the research in this direction. Our dataset Noise-SF will be released at
this https URL

存在对话场景中的未知输入噪声，已有的受监督槽填充模型在实际应用中性能低下；通过引入一个名为 Noise-SF 的噪声鲁棒性评估数据集，本文的提出的框架可以有效地提高模型的鲁棒性。

朝向稳健且通用的训练：基于输入扰动的噪声槽填充的实证研究

Towards Robust and Generalizable Training: An Empirical Study of Noisy  Slot Filling for Input Perturbations

Most existing slot filling models tend to memorize inherent patterns of
entities and corresponding contexts from training data. However, these models
can lead to system failure or undesirable outputs when being exposed to spoken
language perturbation or variation in practice. We propose a perturbed semantic
structure awareness transferring method for training perturbation-robust slot
filling models. Specifically, we introduce two MLM-based training strategies to
respectively learn contextual semantic structure and word distribution from
unsupervised language perturbation corpus. Then, we transfer semantic knowledge
learned from upstream training procedure into the original samples and filter
generated data by consistency processing. These procedures aim to enhance the
robustness of slot filling models. Experimental results show that our method
consistently outperforms the previous basic methods and gains strong
generalization while preventing the model from memorizing inherent patterns of
entities and contexts.

通过传递感知扰动下的语义结构训练鲁棒性槽填充模型，可从上游培训程序中学习语境语义结构和词分布，防止模型记忆实体和语境的固有模式，并通过一致性处理过滤生成数据，以提高槽填充模型的鲁棒性。实验结果表明，这种方法一致优于以往的基本方法，具有良好的泛化性能。

PSSAT: 一种扰动鲁棒槽位填充的扰动语义结构感知传递方法

PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling

The goal of this paper is to use multi-task learning to efficiently scale
slot filling models for natural language understanding to handle multiple
target tasks or domains. The key to scalability is reducing the amount of
training data needed to learn a model for a new task. The proposed multi-task
model delivers better performance with less data by leveraging patterns that it
learns from the other tasks. The approach supports an open vocabulary, which
allows the models to generalize to unseen words, which is particularly
important when very little training data is used. A newly collected
crowd-sourced data set, covering four different domains, is used to demonstrate
the effectiveness of the domain adaptation and open vocabulary techniques.

使用多任务学习方法，缩短新任务学习所需的数据量，以提高自然语言理解中的 slot 填充模型适配多目标任务或领域的效率。该多任务模型可以利用在其他任务中学习到的模式，以较少的数据获得更好的性能，并支持开放词汇，可以很好地应用于微小数据量的训练。实验结果显示了该方法在四个不同领域中的适配效果和开放词汇技术的有效应用。